Can Neural Networks Provide Latent Embeddings for Telemetry-Aware Greedy Routing?
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Abstract (arXiv)
Telemetry-Aware routing promises to increase efficacy and responsiveness to traffic surges in computer networks. Recent research leverages Machine Learning to deal with the complex dependency between network state and routing, but sacrifices explainability of routing decisions due to the black-box nature of the proposed neural routing modules. We propose Placer, a novel algorithm using Message Passing Networks to transform network states into latent node embeddings. These embeddings facilitate quick greedy next-hop routing without directly solving the all-pairs shortest paths problem, and let us visualize how certain network events shape routing decisions.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 31 references that are in the map. First posted on arXiv 13 Feb 2026.
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References · 9
- 2017Neural Message Passing for Quantum Chemistrycited by 3,487
- 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
- 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
- 2019Hyperbolic Graph Convolutional Neural Networkscited by 402
- 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 294
- 2019Hyperbolic Graph Neural Networkscited by 244
- 2022Teal: Learning-Accelerated Optimization of WAN Traffic Engineeringcited by 13
- 2021Packet Routing with Graph Attention Multi-agent Reinforcement Learningcited by 8
- 2024Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamicscited by 2
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